The present invention relates to using a proven social science statistical technique called conjoint analysis to facilitate choices among complex alternatives. More particularly, the present invention relates to methods, systems and computer program products for facilitating individual user choices among complex alternatives using a unique adaptation of conjoint analysis.
As a research methodology, conjoint analysis has been in use in the academic and commercial research community for many years (since the mid-1970""s), and has been commonly used for marketing research purposes to assess consumer preferences among competing products or services.
Generally, conjoint analysis is a tool that researchers use to estimate the relative importance of the attributes that comprise the xe2x80x9calternativesxe2x80x9d in the xe2x80x9cchoice setxe2x80x9d and how much utility each xe2x80x9csettingxe2x80x9d of each xe2x80x9cattributexe2x80x9d has for individuals. Results are often used to simulate the effect on market share that various changes in the xe2x80x9cattribute settingsxe2x80x9d have and thus to fine tune xe2x80x9calternativesxe2x80x9d (e.g. identify the optimal price for a product) and to forecast market share. While many forms of conjoint analysis exist, there are two general defining properties of any conjoint process: 1) each at some point gathers data from individuals by asking each individual to consider (the xe2x80x9cconxe2x80x9d in conjoint) two or more variables simultaneously or jointly (the xe2x80x9cjointxe2x80x9d in conjoint) and 2) each uses the gathered data (responses) to estimate how much utility or value each xe2x80x9cattribute setting.xe2x80x9d Typically, conjoint data is gathered from a sample of users and then analyzed with no flow of information back to the user. Thus, there exists a long-felt need for applications that use conjoint analysis to facilitate individual user choices among complex decisions by providing conjoint analysis results back to the user.
For example, this need is particularly acute in the area of employer-sponsored health plans. Many large- and medium-sized employers offer a number of health plan options for employees. Each health plan includes various features, such as monthly premium, annual deductible, prescription drug coverage, etc. Due to the number of plans and the number of different features of each plan, the choice between plans becomes difficult for the individual employee. Moreover, the employer typically cannot advise an employee to choose one plan over the other because the employer can be held liable if the employer advises an employee to choose a plan that does not pay for some of the employee""s medical expenses. Accordingly, in the employer-sponsored health plan selection process, there exists a long-felt need for methods and systems for facilitating employee choices among health plans.
According to one aspect, the present invention includes a software tool that embodies a xe2x80x9cconjointxe2x80x9d model decision process permitting the simplification of difficult choices among complex alternatives into a sequence of short, simpler decisions. xe2x80x9cAlternativesxe2x80x9d in this context can be products (such as automobiles), services (such as health plans), combinations of complementary services and products, or virtually anything else individuals must decide to choose or not choose. Complex xe2x80x9calternativesxe2x80x9d are those defined in terms of many xe2x80x9cvariablesxe2x80x9d such that in the decision process a lot of information must be considered. Complex xe2x80x9calternativesxe2x80x9d often create difficult decisions that demand that the chooser trade-off the good and bad in each xe2x80x9calternative.xe2x80x9d For example, the choice between a high-quality bicycle versus a low-quality bicycle, given quality is the only criterion used in the selection, is an easy one. However, as the alternatives become more complex, the choice becomes more difficult and trade-offs must be made. The choice between a high-quality, $500 bicycle that comes in pink only versus a low-quality, $100 bicycle that comes in either green, black, or blue is a more difficult decision than that based on quality only.
The present invention uses, at its core, an adaptation of the conjoint model decision process. The use of the conjoint exercise allows to the tool to assist users in making difficult decisions less complex. By going through the exercise, unique profiles of what is important to the user are developed by the application.
In addition to developing user profiles, the present invention, at the end of the exercise, provides users with a xe2x80x9cquality of fitxe2x80x9d measure of how well each product or service available to them meets their unique profile.
In order to facilitate user choices among complex alternatives, the present invention includes computer software that requires an individual user to go through a series of less complex choices. The software first presents the user with a list of features. The user selects features which are of importance to the user. The software then presents the user with a first series of choices requiring the user to input or select first values indicating the relative importance of a best setting and a worst setting of each of the selected features. The user is then presented with a second series of choices requiring the user to input or select second values indicating the relative importance of the user""s preference between first and second pairings of the selected attributes. Each pairing includes a best setting of one attribute and a worst setting of another attribute. The values input by the user in the second series of choices are interpreted as the mathematical difference equal to the relative importance of a best and worst setting of one attribute minus the relative importance of a best and a worst setting for the other attribute in the pairing. A final importance value is calculated for each of the attributes based on the initial relative importance values in the first series of choices and the mathematical difference values. Products and services available to the user are rated based on the final importance values. The user is then presented with data indicating the relative utility to the user of each of the products or services.
Before proceeding, a review of keywords and key phrases and their definitions used in this document is warranted. These keywords are placed in double quotes throughout the document to indicate their use may be somewhat different from common use.
To ensure these keywords and phases are understood, the following example is given.
A person is trying to make a choice between a medium-quality bicycle priced at $250 and a high-quality bicycle priced at $375. The person is given a tool that assists the person in the selection. The tool requires the person to state on a 1-to-5 scale the relative importance of quality and price. For purposes of this example, it is assumed that the person selects 5 and 4, respectively. The values xe2x80x9c5xe2x80x9d and xe2x80x9c4xe2x80x9d are xe2x80x9cimportancexe2x80x9d measures as defined above. The tool also asks the person to rate to what degree the person would prefer a high-quality, $500 bicycle to a low-quality, $100 bicycle. In this example, it is assumed that the person indicates a preference for the higher quality, more expensive bicycle, a xe2x80x9c+1xe2x80x9d on a xe2x88x924-to-+4 scale. The value +1 is a xe2x80x9cdifference in importancexe2x80x9d value as defined above. The tool then computes that the true importance (on a 1-to-5 scale) of quality and price for this person is a 4.7 and a 4.1, respectively. The values 4.7 and 4.1 are xe2x80x9cfinal computed importancexe2x80x9d values, as defined above. These values are used in turn to compute that high-quality is worth 25 (unitless) points to the person whereas medium quality is worth 15 points. The values xe2x80x9c15xe2x80x9d and xe2x80x9c25xe2x80x9d are xe2x80x9csetting utilitiesxe2x80x9d for the quality and price attributes. Similarly, the tool computes that $250 is worth 15 points to the person and $375 is worth 10. Thus, the tool computes that the total worth of the medium-quality bicycle priced at $250 is 30 points (15+15) and that the total worth of the high-quality bicycle priced at $375 is 35 points (25+10). The 30 and 35 point values are xe2x80x9ctotal utilityxe2x80x9d values as defined above. Because 35 is higher than 30, the tool has computed that the medium-quality bicycle priced at $250 is worth slightly less to the person, all things considered, than the high-quality bicycle priced at $375. Thus, the tool recommends that the person should choose the high-quality bicycle priced at $375. The following table provides a summary of examples of each keyword or key phrase from the above example.
One goal in developing the present invention was two-fold. First, create an adaptation of conjoint that is as user friendly as possible (keep it short and easy to understand). Second, go beyond the end of traditional conjoint (developing xe2x80x9cutilitiesxe2x80x9d) and apply these utilities to the performance of a set of products, presenting the user with a sorted list of how well each product meets their stated preferences. In adapting a research statistical technique, traditionally used to study group preferences, and using it to match individual consumer preferences to actual products or services, the present invention application has established a highly useful product in the marketplace.
As mentioned above, conjoint has been in use since the 1970""s. While there are a variety of implementations of the conjoint algorithm in the market the design chosen for implementation of the present invention is exceptionally simple, and yet robust. In addition, the way the present invention has been developed makes it unique. For example, features of real products are evaluated and levels are created, so that all products can be compared by the application in a purely objective basis. The feature attribute descriptions are made as simple and as straightforward as possible. In addition, after the user completes the process of selecting attributes features of the product, making importance of difference decisions, and then trade-off decisions, the application presents results to the user in very simple bar-chart form. The application shows all products available to the user in priority order, based on how well each product matches the preference utility of that individual.
The algorithm utilized by the present invention is unique, in that it provides for paired trade-offs (two by two comparisons of end-point xe2x80x9cattributexe2x80x9d characteristics) instead of the usual more complicated trade-offs involving more than two xe2x80x9cattributesxe2x80x9d defined not only by their end-points (best and worst xe2x80x9csettingsxe2x80x9d) but by numerous xe2x80x9csettingsxe2x80x9d along their entire continuum. As used herein, the term xe2x80x9cendpointsxe2x80x9d refers to the best and worst settings of an attribute. For example, in the bicycle example discussed above, $100 and $500 are endpoints for the price attribute; whereas xe2x80x9chighxe2x80x9d and xe2x80x9clowxe2x80x9d are endpoints for the quality attribute.
According to another aspect of the invention, an xe2x80x9cXxe2x80x9d matrix utilized to estimate xe2x80x9cattributexe2x80x9d xe2x80x9cutilitiesxe2x80x9d. An xe2x80x9cX matrixxe2x80x9d, as described herein, is a configuration of explanatory variable data, or numbers, in a mathematical format. The xe2x80x9cX matrixxe2x80x9d designates the independent variable values used in the ordinary least squares matrix set, whereas the xe2x80x9cY Matrixxe2x80x9d designates the dependent variable values. Examples of X and Y matrices and their use in calculating utilities for attributes will be discussed in more detail below.
Yet another aspect of the invention is the way in which the results of a user""s interaction with the tool are xe2x80x9cfed backxe2x80x9d to the user. For example, each individual""s alternative choices of products, services or concepts are ranked in declining order of xe2x80x9ctotal utility.xe2x80x9d This way of xe2x80x9creportingxe2x80x9d back to each user on how their priorities and decision criteria xe2x80x9cvaluexe2x80x9d each alternative clearly indicate the xe2x80x9cbest fitxe2x80x9d choices among all the alternatives in an individual""s xe2x80x9cchoice set.xe2x80x9d
While one use of the present invention is xe2x80x9cattributexe2x80x9d preference-based decision support tool, the software also simultaneously creates databases of user-level xe2x80x9cpreference dataxe2x80x9d (i.e. xe2x80x9cfinal computed importancexe2x80x9d and xe2x80x9csetting utilityxe2x80x9d data) and other descriptive data. This data has value in the marketplace to producers and middleman organizations as conjoint research and can be used for developing analyses of market share xe2x80x9cattributexe2x80x9d importance, and other outcomes of the decision-making process. The creation and merchandizing of this data is very much a fundamental aspect of the tool""s value.
Currently, the focus of use for the present invention is in the selection of health plans by consumers or employees of medium-sized or large employers. The fringe benefits of most employers of any size typically include health care financing, and it is not unusual to find medium and large employers offering 3, 4 or more health plans to its employees. An employee of those companies uses the present invention to help him/her select that plan from among the alternatives offered which is best suited to him/her. As discussed above, employees are left by their employers to their own resources in selecting a health plan. The major barrier to a more activist policy by employers is one of liability for employee choices. Another barrier is an economic one since uncertain employees commonly consume huge amounts of human resources staffer time seeking guidance in making their choices among health plans. Left to their own devices, employees will seek advice from other employees or may select a plan based on one or a few criteriaxe2x80x94such as premium cost, type of plan or perhaps two or three features of a plan. The present invention alleviates these problems by applying conjoint analysis to facilitate user choices among a large array of complex alternatives.
While the present invention is suitable for assisting employees choose health plans, it has been developed to be generic/flexible enough to be applied to any complex decision. For example, the algorithms described herein are suitable for facilitating user choices among a variety of complex alternative, supplemental insurance, 401k plans/mutual funds, and other product or service categories. Prototypes of this application have been developed, for instance, to assist consumers in choosing computers, and businesses and consumers to choose energy companies. There are many other uses of this application currently being planned.